Interview Question Transformers
Can someone answer the question below. I was asked the question in a data scientist( 8YOE) interview? Why large language models need multi-headed attention layer as appossed to having a single attention layer? Follow up question- During the training process why does the different attention layers get tuned to have different weights?
steppenwolf
Stealth
2 months ago
steppenwolf
Stealth
2 months ago
Umadbro
Stealth
2 months ago
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Data Scientists on
by Gooner7
Goldman Sachs
Neural Machine Translation by Jointly Learning to Align and Translate
If you want more papers like these, drop a "+1" comment below. I will notify these people in DMs next time I upload a new paper. Bahdanau, Cho, and Bengio published a pivotal paper that reshaped the landscape of artificial intelligence, particularly in NLP. This is the first time the world was introduced to the attention mechanism, the most important thing in modern neural machine translation systems. Unlike traditional approaches that relied solely on fixed-length vector representations, the attention mechanism allowed models to dynamically focus on different parts of the input sequence during the translation process. This breakthrough not only significantly improved the accuracy of translation but also enabled the handling of longer sentences with greater fluency. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio not only innovated in the field of machine translation forward but also laid the foundation for attention-based architectures across various domains of deep learning. Their innovative approach demonstrated the power of neural networks to tackle complex sequence-to-sequence tasks and opened doors to a new era of natural language understanding and generation.
Dzmitry Bahdanau, KyungHyun Cho and Yoshua Bengio
https://arxiv.org/pdf/1409.0473
Data Scientists on
by Babel
Yubi
The Research paper that changed the world...
Anything happening in AI today can be traced back to this one brief moment in history... Share your favourite papers. GPT ftw :)
https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf